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Lead AI Engineer

This role is for one of the Weekday's clients

Salary range: Rs 4000000 - Rs 6500000 (ie INR 40-65 LPA)

Min Experience: 5 years

Location: Bengaluru

JobType: full-time

We are seeking a Lead AI Engineer to drive the development and deployment of cutting-edge LLMs (Large Language Models) and agent-based AI systems. This role involves working across the entire AI lifecycle—from identifying use cases to deploying and optimizing models in production. We’re looking for an experienced technical leader who can independently innovate and deliver impactful AI solutions.

Key Responsibilities

  • LLM & NLP Model Development – Design, fine-tune, and deploy large language models (GPT-3/4, T5, BERT, etc.) for tasks such as text generation, summarization, sentiment analysis, and information retrieval.
  • Agentic System Design – Build and deploy autonomous, goal-driven AI agents that can interact with complex environments to accomplish specific objectives.
  • End-to-End Project Ownership – Lead AI initiatives from ideation to production, managing the full model lifecycle independently.
  • Cross-Functional Collaboration – Work closely with product, engineering, and operations teams to transform business requirements into AI-driven solutions, particularly in LLMs and agentic AI.
  • Model Optimization & Evaluation – Enhance models for scalability, accuracy, and efficiency, continuously monitoring and fine-tuning performance in production.
  • Research & Experimentation – Stay updated with the latest advancements in prompt engineering, reinforcement learning, and agent-based AI, assessing their real-world applicability.
  • Documentation & Knowledge Sharing – Maintain detailed documentation of architectures, model performance, and insights while sharing best practices across the organization.

Requirements

  • 5+ years of experience in AI and machine learning, with a strong focus on LLMs and agentic system development.
  • Expertise in NLP & LLMs – Hands-on experience with large language models (GPT-3/4, Claude, etc.), transformer architectures, fine-tuning, and prompt engineering.
  • Agentic AI Development – Proficiency in building and deploying autonomous AI agents using reinforcement learning (RL) or other goal-oriented frameworks.
  • Strong Technical Skills – Advanced proficiency in Python and ML frameworks like Hugging Face Transformers, TensorFlow, PyTorch, along with data processing using Pandas and NumPy.
  • MLOps & Deployment – Experience deploying large-scale AI models and agentic systems using Docker, Kubernetes, MLFlow, or similar MLOps frameworks.
  • Data Management Expertise – Familiarity with data engineering principles, handling large datasets, real-time data processing, and cloud platforms (AWS, GCP, Azure).
  • Independent Problem Solving – Strong analytical and problem-solving skills, with a proven ability to tackle complex AI challenges autonomously.
  • Effective Communication – Ability to articulate technical concepts clearly to non-technical stakeholders and document solutions effectively.

Nice-to-Have

  • Advanced Degree – Master’s or Ph.D. in AI, Machine Learning, Computer Science, or a related field.
  • Finance AI Expertise – Experience applying AI in financial services, with an understanding of compliance, privacy, and domain-specific challenges.
  • Reinforcement Learning Knowledge – Familiarity with RL techniques for developing autonomous, adaptive AI agents.

Average salary estimate

$63000 / YEARLY (est.)
min
max
$48000K
$78000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Lead AI Engineer, Weekday AI

Are you ready to take the helm as the Lead AI Engineer at one of Weekday's prominent clients? This exciting opportunity, based in Bengaluru, offers a competitive salary ranging from Rs 40 LPA to Rs 65 LPA, tailored for seasoned professionals with at least 5 years of experience in the field. In this role, you'll be the driving force behind advanced AI solutions, working closely with cutting-edge large language models (LLMs) and developing autonomous agent-based systems. Imagine designing and fine-tuning models such as GPT-3/4, BERT, and more! Your journey will encompass the entire AI lifecycle—from ideating intriguing use cases to deploying impactful models and optimizing their performance in real-world applications. You'll enjoy collaborating with cross-functional teams, translating complex business needs into practical AI solutions. Plus, your research will help keep the company at the forefront of AI advancements in prompt engineering and reinforcement learning. This position not only demands technical prowess but also excellent communication skills, as you'll articulate sophisticated concepts to stakeholders and share insights across the organization. Join us and be a part of a team where innovation meets excellence and make a tangible difference in the AI landscape.

Frequently Asked Questions (FAQs) for Lead AI Engineer Role at Weekday AI
What are the main responsibilities of a Lead AI Engineer at Weekday's client?

As the Lead AI Engineer, you will oversee the entire AI project life cycle, from idea generation to model deployment. Your key responsibilities include developing and optimizing large language models and agentic systems, collaborating with product and engineering teams, and staying updated on the latest AI technologies. Your insights will contribute significantly to transforming business requirements into effective AI solutions.

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What qualifications do I need to apply for the Lead AI Engineer position at Weekday's client?

To qualify for the Lead AI Engineer role, candidates need a minimum of 5 years of experience in AI and machine learning, particularly with large language models and autonomous systems. Proficiency in tools like Python, TensorFlow, and PyTorch is essential, along with a solid understanding of MLOps practices. Strong communication skills and problem-solving capabilities are also important for success in this role.

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What expertise does Weekday's client seek in LLMs and agentic AI for the Lead AI Engineer role?

Weekday's client seeks candidates with hands-on experience in developing and fine-tuning large language models (such as GPT-3/4) and deploying autonomous AI agents. Familiarity with reinforcement learning and transformative architectures is key, and candidates should demonstrate knowledge in optimizing model performance for real-time applications.

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What is the expected salary range for a Lead AI Engineer at Weekday's client?

The salary range for the Lead AI Engineer position at Weekday's client is competitive, set between Rs 40 LPA to Rs 65 LPA, depending on qualifications and experience. This reflects the organization's commitment to attracting top talent in the AI space and ensuring a rewarding work environment.

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How does collaboration work in the Lead AI Engineer role at Weekday's client?

Collaboration is a significant aspect of the Lead AI Engineer role at Weekday's client. You will engage closely with cross-functional teams, including product managers and engineers, to translate strategic business needs into AI-driven solutions. Effective communication will be crucial to ensure all stakeholders are aligned and informed throughout the project lifecycle.

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Common Interview Questions for Lead AI Engineer
Can you explain your experience with large language models as a Lead AI Engineer?

In answering this question, provide specific examples of projects where you've developed or optimized large language models. Mention the technologies used, the challenges you faced, and the impact of your work on the project or organization.

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How do you ensure the scalability of AI solutions?

Discuss strategies you've implemented to scale AI solutions effectively, such as using MLOps tools like Docker or Kubernetes. Explain how you've optimized models to enhance performance under various loads and your experience with real-time data processing.

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What techniques do you use to evaluate the performance of AI models?

Mention specific evaluation metrics you use to gauge model performance, such as precision, recall, or F1 score. Discuss how you iterate on model design based on these evaluations to ensure optimum accuracy and efficiency.

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How do you approach collaborating with non-technical stakeholders?

Highlight your communication strategies, such as simplifying technical jargon and focusing on how AI solutions meet business goals. Provide examples of successful collaboration with diverse teams to showcase your interpersonal skills.

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What is your process for staying updated on advancements in AI?

Share your strategies for continuous learning, such as following reputable AI journals, attending conferences, or participating in online forums. Mention specific recent advancements that have inspired or informed your work.

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Describe a challenging project you led as a Lead AI Engineer.

Select a challenging project that demonstrates your problem-solving skills, technical expertise, and leadership abilities. Discuss the challenges, actions taken, and the outcomes of the project.

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What role does documentation play in your AI engineering process?

Emphasize the importance of thorough documentation in maintaining model performance, facilitating team communication, and supporting future development. Give examples of documentation practices you adhere to.

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Can you explain your experience with agent-based AI systems?

Discuss specific methodologies for developing autonomous AI agents, including reinforcement learning or specific frameworks you have used. Mention a successful deployment of an agentic AI system and its purposes.

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How do you balance innovation with practicality in AI solutions?

Explain your approach to evaluating new techniques while ensuring feasibility. Share an example of a time you had to pivot from an innovative but impractical approach to a solution that was more pragmatic yet effective.

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How do you prioritize tasks during a comprehensive AI project lifecycle?

Discuss your task prioritization methodology, whether it be Agile, Kanban, or another framework. Provide examples of how efficient prioritization helped streamline project delivery while aligning with strategic goals.

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DATE POSTED
March 29, 2025

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